PROJECT. COLLABORATION. INNOVATION.

SEED FUND 2025

Automated Classification of Construction Data Using LLMs: A Foundation for AI-Driven Analysis

The construction industry faces a persistent challenge: project data is fragmented, inconsistent, and difficult to use for AI analytics. Information is often compiled in datasets (e.g. Excel files) with inconsistent naming conventions. The lack of standardisation in manually created data makes integration and analysis challenging, leaving substantial volumes of valuable information underutilised. Applications such as benchmarking and comparing total project costs and unit rates across projects can provide valuable insights and support decision-making. However, these challenges limit the adoption of Artificial Intelligence (AI) for predictive tasks such as cost estimation, carbon impact assessment, and risk analysis. Manual data handling remains prevalent, reducing efficiency and highlighting the need for AI-based solutions to structure and classify existing construction data.

This project introduces a novel approach using large language models (LLMs) to automate the classification of construction data. Multiple datasets are consolidated into a unified Excel sheet, where the model analyses textual descriptions and predicts three key attributes: material type, family, and placement. Model performance was assessed using precision, recall, F1-score, and confusion matrix analysis. By producing consistently classified, AI-ready datasets, this approach reduces manual intervention, enhances interoperability, and supports future advanced AI analytics. The initiative aligns with national strategies, including the Irish Government’s Housing for All programme, and supports the construction sector’s transition toward a more sustainable and data-driven future.”

Lead RPO: University of Galway

Lead PI: Yadong Jiang

Industry Partners: KOSMOS

Collaborators

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Principal Investigator

Dr Yadong Jiang

Lecturer in Civil Engineering, University of Galway

Dr Yadong Jiang interests include developing novel structures for seismic zones. During his PhD in Earthquake Engineering at IUSS and the University of Pavia, he investigated the seismic performance of concrete-filled steel tube structures, combining experimental testing and numerical analysis. This work refined his laboratory skills, expertise in finite element modelling, and experience in scientific Public – No restrictionation. In 2018, he worked as a post-doctoral researcher at the University of Galway, where he led research on a novel self-centring structural system for seismic zones, conducting material testing and full-scale shake table experiments to validate its seismic performance.Leveraging his laboratory, finite element modelling, and programming skills, his post-doctoral research also focused on optimising the performance of wind and tidal turbine blades. In the OCEANERA-NET SeaBlade project, Dr Jiang designed and analysed an 8-meter rotor blade for one of the largest tidal turbines in the world, investigating the impact of seawater degradation on its structural behaviour. As a recipient of the 2020 SFI Industrial Fellowship, Dr Jiang conducted research as a PI within an industry company, EireComposites, developing a novel methodology for retrofitting end-of-life turbines with high-efficiency rotor blades. These projects led to the development of BladeComp, an in-house software tool designed to accelerate conventional turbine blade design and optimise structural response and cost.In addition to his work on wind and tidal turbine blades, Dr Jiang is committed to research that addresses the broader challenges of climate change and sustainability. His contributions include work on the Enterprise Ireland-funded MI-Drone project, where he studied the structural design of a drone for last-mile delivery to minimise carbon emissions. Dr Jiang is now leading the SEAI-funded FastShip project, exploring the potential of composite materials for manufacturing bow foils, which can significantly improve ship energy efficiency.Dr Jiangs research interests also include the critical area of recyclability and circularity in renewable energy sector, a focus he has pursued since 2022. Leading the SEAI-funded ReBlade project, Dr Jiang examined the potential of recyclable thermoplastic composites for wind turbine blade design and manufacturing. Dr Jiang is also collaborating with researchers at UCD, UCC, and MTU on the SEAI-funded WindLEDeRR project, contributing to the development of solutions for managing end-of-life wind turbines in Ireland. Currently, Dr Jiang is leading a SEAI funded project as PI, investigating the repairable, recyclable and reusable wind turbine blades, including an assessment of environmental impacts.

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Researcher (Construct Innovate WRGP 2024/2025 participant)

Baran Moradkhani

KOSMOS, University of Galway

Learn more about Baran’s background, her experience with the Work Ready Graduate Programme, and details of projects she has been working on at KOSMOS company.

Learn more about Baran